TY  - JOUR
AU  - Glavin, Darragh
AU  - Grua, Eoin Martino
AU  - Nakamura, Carina Akemi
AU  - Scazufca, Marcia
AU  - Ribeiro dos Santos, Edinilza
AU  - Wong, Gloria H Y
AU  - Hollingworth, William
AU  - Peters, Tim J
AU  - Araya, Ricardo
AU  - Van de Ven, Pepijn
PY  - 2023
DA  - 2023/10/19
TI  - Patient Health Questionnaire-9 Item Pairing Predictiveness for Prescreening Depressive Symptomatology: Machine Learning Analysis
JO  - JMIR Ment Health
SP  - e48444
VL  - 10
KW  - Patient Health Questionnaire-2
KW  - PHQ-2
KW  - Patient Health Questionnaire-9
KW  - PHQ-9 items
KW  - depressive symptomatology
KW  - ultrabrief questionnaires
KW  - prescreening
KW  - machine learning
KW  - cardinal symptoms
KW  - low energy
KW  - psychomotor dysfunction
KW  - depressed mood
AB  - Background: Anhedonia and depressed mood are considered the cardinal symptoms of major depressive disorder. These are the first 2 items of the Patient Health Questionnaire (PHQ)–9 and comprise the ultrabrief PHQ-2 used for prescreening depressive symptomatology. The prescreening performance of alternative PHQ-9 item pairings is rarely compared with that of the PHQ-2. Objective: This study aims to use machine learning (ML) with the PHQ-9 items to identify and validate the most predictive 2-item depressive symptomatology ultrabrief questionnaire and to test the generalizability of the best pairings found on the primary data set, with 6 external data sets from different populations to validate their use as prescreening instruments. Methods: All 36 possible PHQ-9 item pairings (each yielding scores of 0-6) were investigated using ML-based methods with logistic regression models. Their performances were evaluated based on the classification of depressive symptomatology, defined as PHQ-9 scores ≥10. This gave each pairing an equal opportunity and avoided any bias in item pairing selection. Results: The ML-based PHQ-9 items 2 and 4 (phq2&4), the depressed mood and low-energy item pairing, and PHQ-9 items 2 and 8 (phq2&8), the depressed mood and psychomotor retardation or agitation item pairing, were found to be the best on the primary data set training split. They generalized well on the primary data set test split with area under the curves (AUCs) of 0.954 and 0.946, respectively, compared with an AUC of 0.942 for the PHQ-2. The phq2&4 had a higher AUC than the PHQ-2 on all 6 external data sets, and the phq2&8 had a higher AUC than the PHQ-2 on 3 data sets. The phq2&4 had the highest Youden index (an unweighted average of sensitivity and specificity) on 2 external data sets, and the phq2&8 had the highest Youden index on another 2. The PHQ-2≥2 cutoff also had the highest Youden index on 2 external data sets, joint highest with the phq2&4 on 1, but its performance fluctuated the most. The PHQ-2≥3 cutoff had the highest Youden index on 1 external data set. The sensitivity and specificity achieved by the phq2&4 and phq2&8 were more evenly balanced than the PHQ-2≥2 and ≥3 cutoffs. Conclusions: The PHQ-2 did not prove to be a more effective prescreening instrument when compared with other PHQ-9 item pairings. Evaluating all item pairings showed that, compared with alternative partner items, the anhedonia item underperformed alongside the depressed mood item. This suggests that the inclusion of anhedonia as a core symptom of depression and its presence in ultrabrief questionnaires may be incompatible with the empirical evidence. The use of the PHQ-2 to prescreen for depressive symptomatology could result in a greater number of misclassifications than alternative item pairings. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2023/1/e48444
UR  - https://doi.org/10.2196/48444
UR  - http://www.ncbi.nlm.nih.gov/pubmed/37856186
DO  - 10.2196/48444
ID  - info:doi/10.2196/48444
ER  -